Customer Stories / Software & Internet / Israel

Wix Logo

How Wix Uses Amazon SageMaker to Solve Multitenant Machine Learning at Scale

Learn how website creation platform Wix unlocked the power of its data with the Wix ML Platform built on AWS.

Reduced time to market

for ML solutions from days to hours

Improved data scientists' efficiency

and productivity with automation

Enhanced transparency and availability

with monitoring capabilities

Saved tens of thousands of dollars per month

with automatic shutdown

Improved user experience

with reduced latency

Overview builds powerful tools that make it simple for anyone to create a website, no coding skills required. Since its founding in 2006, the company has amassed over 254 million users worldwide and created hundreds of products and features to help its users grow and manage their businesses and communities.

Sitting on top of a wealth of data, Wix recognized the potential of applying artificial intelligence and machine learning (ML) to its diverse product offerings. With the support of Amazon Web Services (AWS), it built the Wix ML Platform—an internal continuous integration and continuous deployment framework to facilitate the entire ML pipeline at scale. Using this AWS-powered solution, Wix is automating key data science tasks and empowering its teams to deploy advanced and diverse artificial intelligence use cases at scale.

Doctor Using Computer in Her Office

Opportunity | Using AWS Services to Power ML at Scale for Wix

Headquartered in Tel Aviv, Israel, Wix helps users create, manage, and grow a digital presence. What began as a website builder in 2006 is now a complete platform providing users with enterprise-grade performance, security, and reliable infrastructure. With access to a wide range of commerce and business solutions and advanced SEO and marketing tools, its users have full ownership of their brand, data, and customer relationships so that they can build a powerful digital presence. Wix places a high priority on continuously innovating and delivering new features and products, and the company wanted to take advantage of its user and product data to develop more sophisticated solutions. However, it needed a robust continuous integration and continuous deployment framework to support the ML life cycle.

In 2018, Wix turned to AWS and used several AWS services, including Amazon SageMaker, a service that makes it simple to build, train, and deploy ML models for nearly any use case, to build the Wix ML Platform. On AWS, Wix could access advanced capabilities to accelerate the ML life cycle, such as one-click deployment for Amazon SageMaker and out-of-the-box compatibility with MLflow, an open-source solution used for ML development.

“Most of Wix’s infrastructure is built on AWS, making the integration of the Wix ML Platform with internal services and systems much smoother,” says Itamar Keller, research and development team leader at Wix.


Amazon SageMaker inference helps us deploy models across multiple Availability Zones and runs predictions at scale, either online or in batch mode.”

Itamar Keller
Research and Development Team Leader, Wix

Solution | Saving Time and Deploying Advanced ML Models on AWS

The Wix ML Platform handles critical steps in Wix’s ML life cycle, from data management to model building, training, deployment, and monitoring. Using this solution, Wix data scientists can quickly spin up research environments using Amazon Elastic Compute Cloud (Amazon EC2), a service that provides secure and resizable compute capacity for virtually any workload, without requesting any development resources. They can also seamlessly deploy ML models to make predictions using Amazon SageMaker inference capabilities, which provide a broad selection of ML infrastructure and model deployment options to help meet all of Wix’s ML inference needs. “Amazon SageMaker inference helps us deploy models across multiple Availability Zones and runs predictions at scale, either online or in batch mode,” says Keller.

On AWS, Wix has automated critical tasks in the ML pipeline, making it simpler for data science teams to deploy advanced solutions at scale. For example, the team built a language model for its customer support chatbot. Now, the bot can provide faster and more accurate support to thousands of users per day, powered by Amazon SageMaker training.

“The language model was trained to understand the language specifics of both the domains of Wix in general and customer care specifically,” says Keller. “For example, it understands that Velo is a Wix product. It knows that ‘booking’ in the general language is either the well-known company or the verb booking, but in the Wix language, it is a major product that, if you’re a fitness trainer, lets you manage your appointments.”

In order to adapt to Wix’s dynamic business environment, the chatbot content teams have the freedom to retrain the language model as often as needed, without relying on support from data science or engineering teams. They can effortlessly incorporate new data, evaluate its effectiveness, and train the ML model within a user-friendly environment. Once the new ML model is prepared for deployment, chatbot content team members can swiftly replace the old model and redeploy the new model in production, facilitating uninterrupted availability through blue/green deployment. This approach makes sure that the new (green) fleet is fully operational before transitioning traffic from the old (blue) fleet.

The Wix team also used the platform to optimize its website categorization service, an artificial intelligence–driven solution that automatically determines the business-oriented categories of users’ sites based on their content. Wix has an extensive, highly curated taxonomy of websites; a single website can belong to multiple categories, which is also known as the multilabel classification problem. Now, the Wix team can automatically collect websites that were updated by users within the last 24 hours, run batch predictions using Amazon SageMaker batch inference, and assign the websites to the relevant categories.

“The Wix ML Platform can support hundreds of independent ML models running simultaneously in production,” says Keller. “On AWS, we can modify, train, build, evaluate, and redeploy the full set of models without any involvement from the engineering team and with very minor operations made by data scientists.”

Outcome | Supporting Robust Data Science with Amazon SageMaker

With its AWS-powered ML platform, Wix is empowering its teams to build robust, advanced ML solutions to improve the user experience. The company can bring ML solutions into production within hours instead of days, shortening its time to market. By using multiple AWS Regions, it has also improved the user experience with lower latency.

Automation empowers Wix data scientists to complete their tasks with a single click, improving productivity and efficiency. By taking advantage of AWS monitoring and alerting capabilities, transparency and availability have improved. And by implementing the automatic shutdown of underused resources, the company has saved tens of thousands of dollars in monthly costs.

In the future, Wix will expand the continuous integration and continuous deployment framework’s scale to support the growing needs of its data scientists. Wix also plans to make changes to support lower latency and better cost-resource optimization, further improving its agility.

Wix is dedicated to powering its ML capabilities by using AWS services, open-source tools, and custom solutions. As it continues to drive innovation, Wix relies on AWS to enhance its offerings and help its users build powerful digital presences everywhere.

About Wix

Wix is a platform to create, manage, and grow a digital presence. Founded in 2006, Wix provides more than 254 million users with enterprise-grade performance, security, and reliable infrastructure as well as advanced business and commerce tools.

AWS Services Used

Amazon SageMaker

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

Learn more »

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) offers the broadest and deepest compute platform, with over 700 instances and choice of the latest processor, storage, networking, operating system, and purchase model to help you best match the needs of your workload.

Learn more »

Amazon SageMaker Inference

Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case.

Learn more »

More Software & Internet Customer Stories

no items found 


Get Started

Organizations of all sizes across all industries are transforming their businesses and delivering on their missions every day using AWS. Contact our experts and start your own AWS journey today.